BTEX profile and health risk at the largest bulk port in Latin America, Paranaguá Port.

Port-related activities have a detrimental impact on the air quality both at the point of source and for considerable distances beyond. These activities include, but are not limited to, heavy cargo traffic, onboard, and at-berth emissions. Due to differences in construction, operation, location, and policies at ports, the site-specific air pollution cocktail could result in different human health risks. Thus, monitoring and evaluating such emissions are essential to predict the risk to the community. Environmental agencies often monitor key pollutants (PM2.5, PM10, NO2, SO2), but the volatile organic carbons (VOCs) most often are not, due to its analytical challenging. This study intends to fill that gap and evaluate the VOC emissions caused by activities related to the port of Paranaguá - one of the largest bulk ports in Latin America - by characterizing BTEX concentrations at the port and its surroundings. At seven different sites, passive samplers were used to measure the dispersion of BTEX concentrations throughout the port and around the city at weekly intervals from November 2018 to January 2019. The average and uncertainty of BTEX concentrations (µg m-3) were 0.60 ± 0.43, 5.58 ± 3.80, 3.30 ± 2.41, 4.66 ± 3.67, and 2.82 ± 1.95 for benzene, toluene, ethylbenzene, m- and p-xylene, and o-xylene, respectively. Relationships between toluene and benzene and health risk analysis were used to establish the potential effects of BTEX emissions on the population of the city of Paranaguá. Ratio analysis (T/B, B/T, m,p X/Et, and m,p X/B) indicate that the BTEX levels are mainly from fresh emission sources and that photochemical ageing was at minimum. The cancer risk varied across the sampling trajectory, whereas ethylbenzene represented a moderate cancer risk development for the exposed population in some of the locations. This study provided the necessary baseline data to support policymakers on how to change the circumstances of those currently at risk, putting in place a sustainable operation.

The new WHO air quality guidelines for PM2.5: predicament for small/medium cities.

The global burden of disease estimated that approximately 7.1 million deaths worldwide were related to air pollution in 2016. However, only a limited number of small- and middle-sized cities have air quality monitoring networks. To date, air quality in terms of particulate matter is still mainly focused on mass concentration, with limited compositional monitoring even in mega cities, despite evidence indicating differential toxicity of particulate matter. As this evidence is far from conclusive, we conducted PM2.5 bioaccessibility studies of potentially harmful elements in a medium-sized city, Londrina, Brazil. The data was interpreted in terms of source apportionment, the health risk evaluation and the bioaccessibility of inorganic contents in an artificial lysosomal fluid. The daily average concentration of PM2.5 was below the WHO guideline, however, the chemical health assessment indicated a considerable health risk. The in vitro evaluation showed different potential mobility when compared to previous studies in large-sized cities, those with 1 million inhabitants or more (Curitiba and Manaus). The new WHO guideline for PM2.5 mass concentration puts additional pressure on cities where air pollution monitoring is limited and/or neglected, because decision making is mainly revenue-driven and not socioeconomic-driven. Given the further emerging evidence that PM chemical composition is as, or even more, important than mass concentration levels, the research reported in the paper could pave the way for the necessary inter- and intra-city collaborations that are needed to address this global health challenge.

Atomic Absorption Spectrometry Methods to Access the Metal Solubility of Aerosols in Artificial Lung Fluid.

Recent studies to quantify the health risks that fine particulate matter with an aerodynamic less than 2.5 µm (PM2.5) pose use in vitro approaches. One of these approaches is to incubate PM2.5 in artificial lysosomal fluid for a given period at body temperature. These body fluids used have a high ionic strength and as such can be challenging samples to analyze with atomic spectroscopy techniques. As PM2.5 is a primary health hazard because it is tiny enough to penetrate deep into the lungs and could, in addition, dissolve in the lung fluid it is important to quantify elements of toxic and/or carcinogenic concerns, reliably and accurately. Sophisticated instrumentation and expensive pre-treatment of challenging samples are not always available, especially in developing countries. To evaluate the applicability of graphite furnace atomic absorption spectrometry (GFAAS) without Zeeman correction capability to detect trace quantities of heavy metals leached from PM2.5 on to artificial lung fluid, univariate and multivariate approaches have been used for optimization purposes. The limits of quantification, LOQ, obtained by the optimized method were: 2 µg L-1 (Cu), 3 µg L-1 (Cr), 1 µg L-1 (Mn), and 10 µg L-1 (Pb). The addition/recovery experiments had a mean accuracy of: (Cu) 99 ± 7%; 110 ± 8% (Cr); 95 ± 9% (Mn), and 96 ± 11% (Pb). The average soluble fractions of PM2.5 incubated in artificial lysosomal fluid (ALF) for 1 h were: 1.2 ± 0.01 ng m-3 Cu, 0.4 ± 0.01 ng m-3 Cr, 0.6 ± 0.01 ng m-3 Mn, and 4.8 ± 0.03 ng m-3 Pb. Using historical elemental averages of PM2.5 in Curitiba (Cu 3.3 ng m-3, Cr 2.1 ng m-3, Mn 6.1 ng m-3, Pb 21 ng m-3), the percentage bioaccessibility were determined to be Cu 38%, Cr 20%, Mn 10%, and Pb 23%. The elemental values of the atmospheric soluble fraction of Cu, Cr, and Mn were below the inhalation risk concentrations. However, for Pb, the atmospheric soluble fraction exceeded the inhalation unit risk of 0.012 ng m-3. This robust and straightforward GF AAS method is pivotal for low and middle-income countries were most air pollution adverse effects occur and established lower-cost technologies are likely unavailable.

The influence that different urban development models has on PM2.5 elemental and bioaccessible profiles.

Limited studies have reported on in-vitro analysis of PM2.5 but as far as the authors are aware, bioaccessibility of PM2.5 in artificial lysosomal fluid (ALF) has not been linked to urban development models before. The Brazilian cities Manaus (Amazon) and Curitiba (South region) have different geographical locations, climates, and urban development strategies. Manaus drives its industrialization using the free trade zone policy and Curitiba adopted a services centered economy driven by sustainability. Therefore, these two cities were used to illustrate the influence that these different models have on PM2.5 in vitro profile. We compared PM2.5 mass concentrations and the average total elemental and bioaccessible profiles for Cu, Cr, Mn, and Pb. The total average elemental concentrations followed Mn > Pb > Cu > Cr in Manaus and Pb > Mn > Cu > Cr in Curitiba. Mn had the lowest solubility while Cu showed the highest bioaccessibility (100%) and was significantly higher in Curitiba than Manaus. Cr and Pb had higher bioaccessibility in Manaus than Curitiba. Despite similar mass concentrations, the public health risk in Manaus was higher than in Curitiba indicating that the free trade zone had a profound effect on the emission levels and sources of airborne PM. These findings illustrate the importance of adopting sustainable air quality strategies in urban planning.

Modelling urban cyclists' exposure to black carbon particles using high spatiotemporal data: A statistical approach.

This is a pioneering work in South America to model the exposure of cyclists to black carbon (BC) while riding in an urban area with high spatiotemporal variability of BC concentrations. We report on mobile BC concentrations sampled on 10 biking sessions in the city of Curitiba (Brazil), during rush hours of weekdays, covering four routes and totaling 178 km. Moreover, simultaneous BC measurements were conducted within a street canyon (street and rooftop levels) and at a site located 13 km from the city center. We used two statistical approaches to model the BC concentrations: multiple linear regression (MLR) and a machine-learning technique called random forests (RF). A pool of 25 candidate variables was created, including pollution measurements, traffic characteristics, street geometry and meteorology. The aggregated mean BC concentration within 30-m buffers along the four routes was 7.09 µg m-3, with large spatial variability (5th and 95th percentiles of 1.75 and 16.83 µg m-3, respectively). On average, the concentrations at the street canyon façade (5 m height) were lower than the mobile data but higher than the urban background levels. The MLR model explained a low percentage of variance (24%), but was within the values found in the literature for on-road BC mobile data. RF explained a larger variance (54%) with the additional advantage of having lower requirements for the target and predictor variables. The most impactful predictor for both models was the traffic rate of heavy-duty vehicles. Thus, to reduce the BC exposure of cyclists and residents living close to busy streets, we emphasize the importance of renewing and/or retrofitting the diesel-powered fleet, particularly public buses with old vehicle technologies. Urban planners could also use this valuable information to project bicycle lanes with greater separation from the circulation of heavy-duty diesel vehicles.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks.

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM2.5, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM2.5 levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008-2011 and 2014-2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM2.5 concentrations varied from 0.98 to 54.2 µg m-3, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM2.5, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM2.5 exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Human exposure to hydrogen sulphide concentrations near wastewater treatment plants.

The hydrogen sulphide (H2S) levels from wastewater treatment plants (WWTPs) in Curitiba, Brazil have been quantified for the first time. H2S generated by anaerobic decomposition of organic matter in WWTPs is a cause for concern because it is an air pollutant, which can cause eye and respiratory irritation, headaches, and nausea. Considering the requirement for WWTPs in all communities, it is necessary to assess the concentrations and effects of gases such as H2S on populations living and/or working near WWTPs. The primary objective of this study was to evaluate the indoor and outdoor concentration of H2S in the neighbourhood of two WWTPs located in Curitiba, as well as its human health impacts. Between August 2013 and March 2014 eight sampling campaigns were performed using passive samplers and the analyses were carried out by spectrophotometry, presenting mean concentrations ranging from 0.14 to 32µgm-3. Eleven points at WWTP-A reported H2S average concentrations above the WHO recommendation of 10µgm-3, and 15 points above the US EPA guideline of 2µgm-3. At WWTP-B the H2S concentration was above US EPA guideline at all the sampling points. The I/O ratio on the different sampling sites showed accumulation of indoor H2S in some instances and result in exacerbating the exposure of the residents. The highest H2S concentrations were recorded during the summer in houses located closest to the sewage treatment stations, and towards the main wind direction, showing the importance of these factors when planning a WWTP. Lifetime risk assessments of hydrogen sulphide exposure showed a significant non-carcinogenic adverse health risk for local residents and workers, especially those close to anaerobic WWTPs. The data indicated that WWTPs operated under these conditions should be recognized as a significant air pollution source, putting local populations at risk.

Health implications of atmospheric aerosols from asbestos-bearing road pavements traditionally used in Southern Brazil.

Serpentine and amphibole asbestos occur naturally in certain geologic settings worldwide, most commonly in association with ultramafic rocks, along associated faults. Ultramafic rocks have been used in Piên County, Southern Brazil for decades for the purpose of road paving in rural and urban areas, but without the awareness of their adverse environmental and health impact. The aim of this study was the chemical characterization of aerosols re-suspended in two rural roads of Piên, paved with ultramafic rocks and to estimate the pulmonary deposition of asbestos aerosols. Bulk aerosol samples were analyzed by means of X-ray fluorescence spectrometry and X-ray diffraction analysis, in order to characterize elemental composition and crystallinity. Single-particle compositions of aerosols were analyzed by computer-controlled electron-probe microanalysis, indicating the presence of a few percentages of serpentine and amphibole. Given the chemical composition and size distribution of aerosol particles, the deposition efficiency of chrysotile, a sub-group of serpentine, in two principal segments of the human respiratory system was estimated using a lung deposition model. As an important finding, almost half of the inhaled particles were calculated to be deposited in the respiratory system. Asbestos depositions were significant (∼25 %) in the lower airways, even though the selected breathing conditions (rest situation, nose breathing) implied the lowest rate of respiratory deposition. Considering the fraction of inhalable suspended chrysotile near local roads, and the long-term exposure of humans to these aerosols, chrysotile may represent a hazard, regarding more frequent development of lung cancer in the population of the exposed region.

Influence on the oxidative potential of a heavy-duty engine particle emission due to selective catalytic reduction system and biodiesel blend.

Although the particulate matter (PM) emissions from biodiesel fuelled engines are acknowledged to be lower than those of fossil diesel, there is a concern on the impact of PM produced by biodiesel to human health. As the oxidative potential of PM has been suggested as trigger for adverse health effects, it was measured using the Electron Spin Resonance (OP(ESR)) technique. Additionally, Energy Dispersive X-ray Fluorescence Spectroscopy (EDXRF) was employed to determine elemental concentration, and Raman Spectroscopy was used to describe the amorphous carbon character of the soot collected on exhaust PM from biodiesel blends fuelled test-bed engine, with and without Selective Catalytic Reduction (SCR). OP(ESR) results showed higher oxidative potential per kWh of PM produced from a blend of 20% soybean biodiesel and 80% ULSD (B20) engine compared with a blend of 5% soybean biodiesel and 95% ULSD (B5), whereas the SCR was able to reduce oxidative potential for each fuel. EDXRF data indicates a correlation of 0.99 between concentration of copper and oxidative potential. Raman Spectroscopy centered on the expected carbon peaks between 1100cm(-1) and 1600cm(-1) indicate lower molecular disorder for the B20 particulate matter, an indicative of a more graphitic carbon structure. The analytical techniques used in this study highlight the link between biodiesel engine exhaust and increased oxidative potential relative to biodiesel addition on fossil diesel combustion. The EDXRF analysis confirmed the prominent role of metals on free radical production. As a whole, these results suggest that 20% of biodiesel blends run without SCR may pose an increased health risk due to an increase in OH radical generation.

Composition of PM2.5 and PM1 on high and low pollution event days and its relation to indoor air quality in a home for the elderly.

Many studies probing the link between air quality and health have pointed towards associations between particulate matter (PM) exposure and decreased lung function, aggravation of respiratory diseases like asthma, premature death and increased hospitalisation admissions for the elderly and individuals with cardiopulmonary diseases. Of recent, it is believed that the chemical composition and physical properties of PM may contribute significantly to these adverse health effects. As part of a Belgian Science Policy project ("Health effects of particulate matter in relation to physical-chemical characteristics and meteorology"), the chemical composition (elemental and ionic compositions) and physical properties (PM mass concentrations) of PM were investigated, indoors and outdoors of old age homes in Antwerp. The case reported here specifically relates to high versus normal/low pollution event periods. PM mass concentrations for PM1 and PM2.5 fractions were determined gravimetrically after collection via impaction. These same samples were hence analysed by EDXRF spectrometry and IC for their elemental and ionic compositions, respectively. During high pollution event days, PM mass concentrations inside the old age home reached 53 µg m(-3) and 32 µg m(-3) whilst outside concentrations were 101 µg m(-3) and 46 µg m(-3) for PM2.5 and PM1, respectively. The sum of nss-sulphate, nitrate and ammonium, dominate the composition of PM, and contribute the most towards an increase in the PM during the episode days constituting 64% of ambient PM2.5 (52 µg m(-3)) compared to 39% on non-episode days (10 µg m(-3)). Other PM components, such as mineral dust, sea salt or heavy metals were found to be considerably higher during PM episodes but relatively less important. Amongst heavy metals Zn and Pb were found at the highest concentrations in both PM2.5 and PM1. Acid-base ionic balance equations were calculated and point to acidic aerosols during event days and acidic to alkaline aerosols during non-event days. No significant sources of indoor pollutants could be identified inside the old-age home as high correlations were found between outdoor and indoor PM, confirming mainly the outdoor origin of indoor air.